A power grid optimization iterative solving method based on time-series production simulation

Abstract

This invention discloses an iterative solution method for power grid optimization based on time-series production simulation, belonging to the field of power system optimization and operation control. The method includes: building a time-series production simulation model and performing preliminary solutions; calculating power flow based on the preliminary solution results to obtain time-period transmission loss data and feeding it back into the power balance constraints for further solving; performing power flow calculations based on the unit output results considering transmission losses, and identifying periods with insufficient safety margins through composite constraint risk identification; and establishing a composite time-series production simulation model based on the set of periods with insufficient safety margins and iteratively solving it. This invention introduces transmission losses and grid constraints into the model, which can improve solution efficiency, reduce computational complexity, and enhance the security and accuracy of power grid optimization results.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization and operation control, and particularly relates to an iterative solution method for power grid optimization based on time-series production simulation. Background Technology

[0002] As the proportion of new energy sources in the power system continues to increase, wind and solar power output exhibits significant randomness and volatility, leading to greater uncertainty and complexity in grid operation. Traditional time-series production simulations typically rely on economic dispatch models for thermal power units, optimizing the active and reactive power output of each unit to minimize the total system operating cost. However, these traditional models often neglect the impact of the grid structure on power transmission and node voltage, potentially resulting in problems such as insufficient node voltage, line power flow, and insufficient safety margins in unit output during actual operation.

[0003] When time-series power generation simulation models do not consider grid constraints, they may assume that renewable energy can be freely transmitted within the system, resulting in higher renewable energy absorption capacity and lower wind and solar curtailment. In this case, the calculated renewable energy absorption rate is often idealized. Therefore, there is an urgent need for an optimization method that can balance the economic efficiency and security of the power grid throughout all time periods to improve the solution efficiency and accuracy of time-series power generation simulations in power grids containing wind and solar power. Summary of the Invention

[0004] The purpose of this invention is to provide an iterative solution method for power grid optimization based on time-series production simulation, in order to solve the problems in existing time-series production simulation methods that do not fully consider transmission losses and power grid constraints, resulting in idealized calculation results of renewable energy absorption rate, insufficient safety margin in power flow verification, and large model size and low solution efficiency when introducing grid constraints throughout the time period.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An iterative solution method for power grid optimization based on time-series production simulation, the method comprising the following steps:

[0007] Step 1: Obtain time-series data of grid load, wind power output, photovoltaic power output, and thermal power unit parameters. Set differentiated scheduling weights for units of the same model or with the same operating cost. Add the differentiated scheduling weights to the operating cost coefficient of the corresponding unit to build a time-series production simulation model. With the goal of minimizing the total system operating cost, solve the optimization time period to obtain the preliminary power allocation scheme and unit output results.

[0008] Step 2: Based on the preliminary power allocation scheme and unit output results, power flow calculation is performed in combination with grid nodes and grid structure to obtain time-period transmission loss data. The transmission loss data is then fed back to the power balance constraints in the time-series production simulation model for further solution to obtain the unit output results after considering transmission losses.

[0009] Step 3: Based on the unit output results after considering transmission losses, and combined with the grid nodes and grid structure, perform power flow calculations to obtain power flow data within the optimized time period. Perform composite constraint risk identification on the power flow data. Based on at least two types of safety margin indicators among node voltage safety margin, line active power flow safety margin, active / reactive power output safety margin of thermal power units, and line phase angle difference safety margin, identify the time periods with insufficient safety margins and incorporate them into a preset set of initially empty time periods with insufficient safety margins.

[0010] Step 4: Based on the set of time periods with insufficient safety margin, establish a composite time-series production simulation model for the optimized time period and solve it uniformly. The composite time-series production simulation model adopts a time-series production simulation model that considers transmission losses for time periods not included in the set of time periods with insufficient safety margin, and adopts a time-series production simulation model that considers grid constraints for time periods that belong to the set of time periods with insufficient safety margin.

[0011] Step 5: Calculate the power output of the units obtained from the composite time-series production simulation model, combining the power grid nodes and grid structure. Then, identify composite constraint risks in the power flow data again. If there are any new periods with insufficient safety margins, add them to the set of periods with insufficient safety margins. Repeat steps 4 and 5 until there are no periods with insufficient safety margins in any time period and no new periods with insufficient safety margins are added. Complete the iterative solution and output the optimized results that satisfy the constraints.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention introduces transmission loss feedback on the basis of time-series production simulation, and introduces transmission loss constraints, apparent power constraints and corresponding linearization processing in the time period considering grid constraints, so that the optimization results can more accurately reflect the actual operating state of the power grid. (2) The present invention identifies the time period of insufficient safety margin through power flow calculation, and introduces the time-series production simulation model considering grid constraints only in the time period of insufficient safety margin, avoiding the problem of excessive model size and long solution time caused by considering grid constraints in all time periods, thereby reducing the computational complexity and improving the solution efficiency. (3) The present invention can consider the impact of grid structure, line transmission capacity and transmission loss on wind power and photovoltaic absorption, reduce the overestimation of new energy absorption capacity by traditional time-series production simulation methods when grid constraints are not considered, and make the calculation results of new energy absorption rate more accurate. Attached Figure Description

[0013] Figure 1 This is an overall flowchart of an iterative solution method for power grid optimization based on time-series production simulation;

[0014] Figure 2 A schematic diagram of phase angle linearization constrained by line loss;

[0015] Figure 3 This is a schematic diagram of apparent power linearization. Detailed Implementation

[0016] The present invention will now be further described with reference to the accompanying drawings.

[0017] like Figure 1 As shown, an iterative solution method for power grid optimization based on time-series production simulation is proposed, the method comprising the following steps:

[0018] Step 1: Obtain time-series data of grid load, wind power output, photovoltaic power output, and thermal power unit parameters. Set differentiated scheduling weights for units of the same model or with the same operating cost. Add the differentiated scheduling weights to the operating cost coefficient of the corresponding unit to build a time-series production simulation model. With the goal of minimizing the total system operating cost, solve the optimization time period to obtain the preliminary power allocation scheme and unit output results.

[0019] Step 2: Based on the preliminary power allocation scheme and unit output results, power flow calculation is performed in combination with grid nodes and grid structure to obtain time-period transmission loss data. The transmission loss data is then fed back to the power balance constraints in the time-series production simulation model for further solution to obtain the unit output results after considering transmission losses.

[0020] Step 3: Based on the unit output results after considering transmission losses, and combined with the grid nodes and grid structure, perform power flow calculations to obtain power flow data within the optimized time period. Perform composite constraint risk identification on the power flow data. Based on at least two types of safety margin indicators among node voltage safety margin, line active power flow safety margin, active / reactive power output safety margin of thermal power units, and line phase angle difference safety margin, identify the time periods with insufficient safety margins and incorporate them into a preset set of initially empty time periods with insufficient safety margins.

[0021] Step 4: Based on the set of time periods with insufficient safety margin, establish a composite time-series production simulation model for the optimized time period and solve it uniformly. The composite time-series production simulation model adopts a time-series production simulation model that considers transmission losses for time periods not included in the set of time periods with insufficient safety margin, and adopts a time-series production simulation model that considers grid constraints for time periods that belong to the set of time periods with insufficient safety margin.

[0022] Step 5: Calculate the power output of the units obtained from the composite time-series production simulation model, combining the power grid nodes and grid structure. Then, identify composite constraint risks in the power flow data again. If there are any new periods with insufficient safety margins, add them to the set of periods with insufficient safety margins. Repeat steps 4 and 5 until there are no periods with insufficient safety margins in any time period and no new periods with insufficient safety margins are added. Complete the iterative solution and output the optimized results that satisfy the constraints.

[0023] The power flow calculation, which combines power grid nodes and grid structure, specifically includes the following process:

[0024] First, a node model is established based on the number of power grid nodes, and a transmission line model is established based on parameters such as the first and last nodes of the line, line resistance, line reactance, and line capacity. At the same time, load models, wind power output models, photovoltaic power output models, and thermal power unit models are set at each node, and balancing nodes are set for power balance in power flow calculation.

[0025] Then, according to the optimized time period, the active power output, reactive power output, unit start-up and shutdown status, wind power absorption capacity, and photovoltaic power absorption capacity of the thermal power units obtained from the time-series production simulation are read time by time period, and the active and reactive loads of each time period are written into the corresponding nodes. For thermal power units that are in the start-up state, they are used as generator nodes to participate in the power flow calculation; for thermal power units that are in the shutdown state, they are not included in the power flow calculation for that time period.

[0026] Furthermore, AC power flow calculations are performed for each time period to obtain the voltage amplitude, phase angle, active power flow, reactive power flow, active power loss, and reactive power loss of each line. When there is still an imbalance in active or reactive power at the balancing node, the unbalanced power is distributed within the upper and lower limits of the unit output according to the regulation capacity of the online units, and power flow calculations are performed again until the set power balance accuracy requirements are met.

[0027] Finally, the power flow calculation results for each time period are summarized to obtain the time-period transmission active power loss, transmission reactive power loss, node voltage, node phase angle, and line power flow data. Among them, the time-period transmission loss data is used to feed back into the power balance constraints of the time-series production simulation model, while the node voltage, node phase angle, and line power flow data are used for subsequent composite constraint risk identification.

[0028] The construction of the time-series production simulation model includes the following:

[0029] Objective function: In the formula, This represents the total operating cost of the system. This represents the total operating cost of the thermal power unit. The total cost of wind curtailment; The total cost of curtailment; Indicates time; for A collection of moments; For thermal power units A set; For thermal power units Operating costs; For thermal power units exist Active power at any given moment; For wind farm A set; The penalty cost coefficient for wind curtailment; For wind farm exist Wind power is always available; For wind farm exist Actual power consumption at any given time; For photovoltaic fields A set; The penalty cost coefficient for abandoning light; For photovoltaic fields exist Photovoltaic power is always available; For photovoltaic fields exist Actual power consumption at any given time;

[0030] Power balance constraints: In the formula, for Active load at any given moment; for Active power loss during power transmission at any given moment; For thermal power units exist Reactive power at any given moment; for Reactive load at any given moment; for Reactive power loss during power transmission at any given moment;

[0031] Thermal power unit output constraints: In the formula, and These are the minimum and maximum active power outputs of thermal power units, respectively. and These are the minimum and maximum reactive power outputs of thermal power units, respectively.

[0032] Thermal power unit ramp / downhill constraints: In the formula, For thermal power units exist Active power at any given moment; and thermal power units Uphill and downhill capabilities;

[0033] Minimum start-up and shutdown time constraints for thermal power units: In the formula, For thermal power units exist The system is always on or off; 0 indicates off and 1 indicates on. , Minimum start-up and shutdown time for thermal power units; , For thermal power units exist The startup and shutdown states at any given time;

[0034] Wind and solar power output constraints:

[0035] Thermal power unit operating logic constraints: In the formula, For thermal power units exist The portion of the energy output that exceeds the minimum active power at any given moment; and This refers to the unit's ability to start and stop on slopes and at high speeds. For thermal power units exist The startup status at any given moment; For thermal power units exist The state of being constantly off; Time series The last period; For thermal power units exist The portion of the energy output that exceeds the minimum active power at any given moment; For thermal power units exist The machine is constantly on and off.

[0036] The data that needs to be judged for the composite constraint risk identification of power flow data includes:

[0037] Node voltage safety margin assessment: In the formula, For nodes exist Voltage amplitude at any given moment; For nodes A set; and For nodes The minimum and maximum permissible voltage values; if or Then determine There is always a risk of insufficient safety margin in node voltage;

[0038] Determining the active power flow safety margin of a transmission line: In the formula, for Timetable The meritorious trend; For the line Maximum active transmission capacity; A 0-1 variable, representing the line Does it exist? If the line exists, then... It is 1 if it is true, otherwise it is 0; For the line The set; if or Then determine There is always a problem of insufficient safety margin in the active power flow of the line;

[0039] Determining the safety margin of active / reactive power output of thermal power units: If the active or reactive power output of the generator unit does not meet the above range, then it is determined that... There is always a lack of safety margin in the active / reactive power output of thermal power units;

[0040] Line phase angle difference safety margin determination: In the formula, for Timetable The phase angle difference between the two endpoints; For the line Maximum allowable phase angle difference; if or Then determine There is always an insufficient safety margin for the phase angle difference of the line;

[0041] If the power flow data has insufficient safety margin, the corresponding time period will be identified as the insufficient safety margin period and incorporated into the insufficient safety margin period set.

[0042] The time-series production simulation considering network constraints changes the power balance constraint to a node power balance constraint based on the original architecture: In the formula, For nodes thermal power units at the location A set; For nodes Wind farm A set; For nodes Photovoltaic field A set; For nodes In Active load at any given moment; For nodes A set of connected lines; for Timetable The active power loss during power transmission; For periods with insufficient safety margins; For nodes In Reactive load at any given moment; for Timetable The unproductive current; for Timetable The reactive power loss during power transmission;

[0043] The time-series production simulation model that considers the space frame constraints also includes the following constraints:

[0044] Line power flow capacity constraints:

[0045] Power flow constraints on the line: In the formula, and The lines are respectively The beginning and end are at Voltage amplitude at time 10:00 For the line electrical conductivity, For the line The susceptance; neglecting higher-order variables and and A first-order Taylor expansion yields the linearized power flow constraints. In the formula, As a preset constant, in this embodiment, The value can be taken as 1000, when the line When it exists, the line has variables. =1, the line power flow constraint is normally enabled, used to describe the active and reactive power flow on the line. When it does not exist, the line has variables. =0, the line power flow constraint is relaxed and no longer enforced; and for Phase difference at time Auxiliary variables;

[0046] like Figure 2 As shown, line loss constraints: For the formula Piecewise linearization is performed to obtain In the formula, For segmented indexes; For segmented indexes A set; The number of piecewise linearized blocks; For the line exist The first phase angle difference at time The values ​​at each segment; the line loss constraint includes the phase angle difference squared term, which is a nonlinear constraint. This invention divides the allowable phase angle difference range of the line into several segments, and uses a piecewise linearization method to approximate the phase angle difference squared term, introducing phase angle difference auxiliary variables and segment variables to transform the line loss constraint into a linear constraint.

[0047] Line phase angle constraints:

[0048] Node voltage amplitude constraints:

[0049] like Figure 3 As shown, apparent power constraints: In the formula, For the line Maximum apparent power capacity; For linearization of apparent power, segmented indexing; Number of apparent power linearization blocks; Piece index used for apparent power linearization A set; For the line exist Time of the first The equivalent upper limit of the dividing line; It is the reactive power axis and the first The angle between the perpendicular lines of the dividing line; For the first The angle of the dividing line is half of the corresponding angle. The apparent power constraint is a circular feasible region constraint composed of active power and reactive power. This invention uses multiple linear boundaries to approximate the circular apparent power constraint, transforming the apparent power constraint into a linear constraint.

[0050] To verify the effectiveness of the method of this invention, a Garver 6-node system was selected as a case study, and wind power and photovoltaic output data were incorporated. The calculation results of the renewable energy absorption rate of the conventional time-series production simulation model and the method of this invention under different calculation periods were compared and analyzed. The results are shown in Table 1:

[0051] Table 1 Comparison of renewable energy absorption rates under different calculation periods: Model type 240 hours 2400 hours 8760 hours Conventional model 93.81% 89.10% 89.84% This invention model 88.93% 83.81% 87.16% The results show that conventional time-series production simulation models do not fully consider the impact of grid constraints and transmission losses on the renewable energy transmission capacity, resulting in relatively high renewable energy absorption rates. In contrast, the method of this invention, by introducing transmission loss feedback and identifying the combined constraint risk of insufficient safety margin periods, can reflect the impact of limited line transmission capacity on wind and solar power absorption, making the calculated renewable energy absorption rate closer to the actual grid operation.

Claims

1. An iterative solution method for power grid optimization based on time-series production simulation, characterized in that, The method includes the following steps: Step 1: Obtain time-series data of grid load, wind power output, photovoltaic power output, and thermal power unit parameters. Set differentiated scheduling weights for units of the same model or with the same operating cost. Add the differentiated scheduling weights to the operating cost coefficient of the corresponding unit to build a time-series production simulation model. With the goal of minimizing the total system operating cost, solve the optimization time period to obtain the preliminary power allocation scheme and unit output results. Step 2: Based on the preliminary power allocation scheme and unit output results, power flow calculation is performed in combination with grid nodes and grid structure to obtain time-period transmission loss data. The transmission loss data is then fed back to the power balance constraints in the time-series production simulation model for further solution to obtain the unit output results after considering transmission losses. Step 3: Based on the unit output results after considering transmission losses, and combined with the grid nodes and grid structure, perform power flow calculations to obtain power flow data within the optimized time period. Perform composite constraint risk identification on the power flow data. Based on at least two types of safety margin indicators among node voltage safety margin, line active power flow safety margin, active / reactive power output safety margin of thermal power units, and line phase angle difference safety margin, identify the time periods with insufficient safety margins and incorporate them into a preset set of initially empty time periods with insufficient safety margins. Step 4: Based on the set of time periods with insufficient safety margin, establish a composite time-series production simulation model for the optimized time period and solve it uniformly. The composite time-series production simulation model adopts a time-series production simulation model that considers transmission losses for time periods not included in the set of time periods with insufficient safety margin, and adopts a time-series production simulation model that considers grid constraints for time periods that belong to the set of time periods with insufficient safety margin. Step 5: Calculate the power output of the units obtained from the composite time-series production simulation model, combining the power grid nodes and grid structure. Then, identify composite constraint risks in the power flow data again. If there are any new periods with insufficient safety margins, add them to the set of periods with insufficient safety margins. Repeat steps 4 and 5 until there are no periods with insufficient safety margins in any time period and no new periods with insufficient safety margins are added. Complete the iterative solution and output the optimized results that satisfy the constraints.

2. The iterative solution method for power grid optimization based on time-series production simulation according to claim 1, characterized in that: Step one, the construction of the time-series production simulation model includes the following: Objective function: In the formula, The total operating cost of the system; This represents the total operating cost of the thermal power unit. The total cost of wind curtailment; The total cost of curtailment; Indicates time; for A collection of moments; For thermal power units A set; For thermal power units Operating costs; For thermal power units exist Active power at any given moment; For wind farm A set; The penalty cost coefficient for wind curtailment; For wind farm exist Wind power is always available; For wind farm exist Actual power consumption at any given time; For photovoltaic fields A set; The penalty cost coefficient for abandoning light; For photovoltaic fields exist Photovoltaic power is always available; For photovoltaic fields exist Actual power consumption at any given time; Power balance constraints: In the formula, for Active load at any given moment; for Active power loss during power transmission at any given moment; For thermal power units exist Reactive power at any given moment; for Reactive load at any given moment; for Reactive power loss during power transmission at any given moment; Thermal power unit output constraints: In the formula, and These are the minimum and maximum active power outputs of thermal power units, respectively. and These are the minimum and maximum reactive power outputs of thermal power units, respectively. Thermal power unit ramp / downhill constraints: In the formula, For thermal power units exist Active power at any given moment; and thermal power units Uphill and downhill capabilities; Minimum start-up and shutdown time constraints for thermal power units: In the formula, For thermal power units exist The system is always on or off; 0 indicates off and 1 indicates on. , Minimum start-up and shutdown time for thermal power units; , For thermal power units exist The startup and shutdown states at any given time; Wind and solar power output constraints: Thermal power unit operating logic constraints: In the formula, For thermal power units exist The portion of the energy output that exceeds the minimum active power at any given moment; and This refers to the unit's ability to start and stop on slopes and at high speeds. For thermal power units exist The startup status at any given moment; For thermal power units exist The state of being constantly off; Time series The last period; For thermal power units exist The portion of the energy output that exceeds the minimum active power at any given moment; For thermal power units exist The machine is constantly on and off.

3. The iterative solution method for power grid optimization based on time-series production simulation according to claim 1, characterized in that: In steps three and five, the data that needs to be judged for the composite constraint risk identification of power flow data includes: Node voltage safety margin assessment: In the formula, For nodes exist Voltage amplitude at any given moment; For nodes A set; and For nodes The minimum and maximum permissible voltage values; if or Then determine There is always a risk of insufficient safety margin in node voltage; Determining the active power flow safety margin of a transmission line: In the formula, for Timetable The meritorious trend; For the line Maximum active transmission capacity; A 0-1 variable, representing the line Does it exist? If the line exists, then... It is 1 if it is true, otherwise it is 0. For the line The set; if or Then determine There is always a problem of insufficient safety margin in the active power flow of the line; Determining the safety margin of active / reactive power output of thermal power units: like or Then determine There is always a risk of insufficient safety margin in the active power output of thermal power units; if or Then determine There is always a lack of safety margin for reactive power output of thermal power units; Line phase angle difference safety margin determination: In the formula, for Timetable The phase angle difference between the two endpoints; For the line Maximum allowable phase angle difference; if or Then determine There is always an insufficient safety margin for the phase angle difference of the line; If the power flow data has insufficient safety margin, the corresponding time period will be identified as the insufficient safety margin period and incorporated into the insufficient safety margin period set.

4. The iterative solution method for power grid optimization based on time-series production simulation according to claim 1, characterized in that: In step four, the time-series production simulation model considering grid constraints is obtained by replacing the power balance constraints with node power balance constraints on the time-series production simulation model considering transmission losses. In the formula, For nodes thermal power units at the location A set; For nodes Wind farm A set; For nodes Photovoltaic field A set; For nodes In Active load at any given moment; For nodes A set of connected lines; for Timetable The active power loss during power transmission; For periods with insufficient safety margins; For nodes In Reactive load at any given moment; for Timetable The unproductive current; for Timetable The reactive power loss during power transmission; The time-series production simulation model considering the space frame constraints also includes the following constraints: Line power flow capacity constraints: Power flow constraints: In the formula, and The lines are respectively The beginning and end are at Voltage amplitude at time 10:00 For the line electrical conductivity, For the line The susceptance; neglecting higher-order variables and and and A first-order Taylor expansion yields the linearized power flow constraints. In the formula, This is a preset constant; and for Phase difference at time Auxiliary variables; Line loss constraints: For the formula Piecewise linearization is performed to obtain In the formula, For segmented indexes; For segmented indexes A set; The number of piecewise linearized blocks; For the line exist The first phase angle difference at time The value at each block; Line phase angle constraints: Node voltage amplitude constraints: Apparent power constraint: In the formula, For the line Maximum apparent power capacity; For linearization of apparent power, segmented indexing; Number of apparent power linearization blocks; Piece index used for apparent power linearization A set; For the line exist Time of the first The equivalent upper limit of the dividing line; It is the reactive power axis and the first The angle between the perpendicular lines of the dividing line; For the first Half of the angle corresponding to the dividing line.

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